Assessing the impacts of correlated variability with dissociated timescales

Abstract

Despite the profound influence on coding capacity of sensory neurons, the measurements of noise correlations have been inconsistent. This is, possibly, because nonstationarity, i.e. drifting baselines, engendered the spurious long-term correlations even if no actual short-term correlation existed. Although attempts to separate them have been made previously, they were ad hoc for specific cases or computationally too demanding. Here we proposed an information-geometric method to unbiasedly estimate pure short-term noise correlations irrespective of the background brain activities without demanding computational resources. First, the benchmark simulations demonstrated that the proposed estimator is more accurate and computationally e cient than the conventional correlograms and the residual correlations with Kalman filters or moving averages of length three or more, while the best moving average of length two coincided with the propose method regarding correlation estimates. Next, we analyzed the cat V1 neural responses to demonstrate that the statistical test accompanying the proposed method combined with the existing nonstationarity test enabled us to dissociate short- and long-term noise correlations. When we excluded the spurious noise correlations of purely long-term nature, only small fraction of neuron pairs showed significant short-term correlations, possibly reconciling the previous inconsistent observations on existence of significant noise correlations. The decoding accuracy was slightly improved by the short-term correlations. Although the long-term correlations deteriorated the generalizability, the generalizability was recovered by the decoder with trend removal, suggesting that brains could overcome nonstationarity. Thus the proposed method enables us to elucidate the impacts of short- and long-term noise correlations in a dissociated manner.

Significance statement The proposed measure for spike-count noise correlations, based on the local temporal detrending, enables us to decompose the correlated responses into long- and short-timescale components. The proposed method is essential to elucidate the population codes in the era of large-scale electrophysiology as it works for large number of simultaneously recorded neurons while existing methods do not. With the additional help of the machine learning that classifies stimuli from neural activities, we demonstrate proper ways to assess the impacts on decoding of the presence of short- or long-term noise correlations, separately. The well-designed decoding analysis with dissociated correlated activities will help to gain insight into the brain’s decoding strategies under changing environments.